Minimal Intervention Research Articles

Sim-to-real transfer of adaptive control parameters for AUV stabilisation under current disturbance

Learning-based adaptive control methods hold the potential to empower autonomous agents in mitigating the impact of process variations with minimal human intervention. However, their application to autonomous underwater vehicles (AUVs) has been constrained by two main challenges: (1) the presence of unknown dynamics in the form of sea current disturbances, which cannot be modelled or measured due to limited sensor capability, particularly on smaller low-cost AUVs, and (2) the nonlinearity of AUV tasks, where the controller response at certain operating points must be excessively conservative to meet specifications at other points. Deep Reinforcement Learning (DRL) offers a solution to these challenges by training versatile neural network policies. Nevertheless, the application of DRL algorithms to AUVs has been predominantly limited to simulated environments due to their inherent high sample complexity and the distribution shift problem. This paper introduces a novel approach by combining the Maximum Entropy Deep Reinforcement Learning framework with a classic model-based control architecture to formulate an adaptive controller. In this framework, we propose a Sim-to-Real transfer strategy, incorporating a bio-inspired experience replay mechanism, an enhanced domain randomisation technique, and an evaluation protocol executed on a physical platform. Our experimental assessments demonstrate the effectiveness of this method in learning proficient policies from suboptimal simulated models of the AUV. When transferred to a real-world vehicle, the approach exhibits a control performance three times higher compared to its model-based nonadaptive but optimal counterpart.

Automated Idiopathic Normal-Pressure Hydrocephalus Diagnosis via Artificial Intelligence-Based 3D T1 MRI Volumetric Analysis.

Idiopathic normal pressure hydrocephalus (iNPH) is reversible dementia, that is underdiagnosed. The purpose of this study was to develop an automated diagnostic method for iNPH using artificial intelligence techniques with a T1-weighted MRI scan. We quantified iNPH, Parkinson's disease, Alzheimer's disease, and healthy control patients on T1-weighted 3D brain MRI scans using 452 scans for training and 110 scans for testing. Automatic component measurement algorithms were developed for Evans' index, Sylvian fissure enlargement, high-convexity tightness, callosal angle, and normalized lateral ventricle volume. XGBoost models were trained for both automated measurements and manual labels for iNPH prediction. A total of 452 patients (200 men; mean age ± standard deviation, 73.2 ± 6.5 years) were included in the training set. Of the 452 patients, 111 (24.6%) had iNPH. We obtained AUC values of 0.956 for automatically measured high-convexity tightness and 0.830 for Sylvian fissure enlargement. Intra-class correlation values of 0.824 for the callosal angle and 0.924 for Evans' index were measured. Using the decision tree of the XGBoost model, the model trained on manual labels obtained an average cross-validation AUC of 0.988 on the training set and 0.938 on the unseen test set, while the fully automated model obtained a cross-validation AUC of 0.983 and an unseen test AUC of 0.936. We demonstrated a machine-learning algorithm capable of diagnosing iNPH from a 3D T1-weighted MRI scan that is robust to the failure. We propose a method to scan large numbers of 3D T1-weighted MRI scans with minimal human intervention, making possible large-scale iNPH screening. iNPH = idiopathic normal-pressure hydrocephalus; PD = Parkinson's disease; AD = Alzheimer's disease; HC = healthy control; CSF = cerebrospinal fluid; DESH = disproportionately enlarged subarachnoid space hydrocephalus; 3D = three-dimensional.

Advancements in Telemedicine: Enhancing Public Health Outcomes through the Implementation of 5G Networks for Ultra Low Latency Communication

With the introduction of 5G networks, a new era emerges in telemedicine, dramatically improving public health outcomes with fewer telecommunications networks. The proliferation of 5G communications networks that enable seamless remote access, routine patient monitoring, and expedited treatment on mobile devices is the driving force behind improved patient care and public health risk variety detection and maintenance. High infrastructure costs, wide adoption, and data privacy and security Despite the compelling potential of 5G in telemedicine, some barriers to adoption have been proposed for Integrating Smart Automated Healthcare Analysis (I-SAHA), using artificial intelligence (AI) and machine learning (ML). Using these, I-SAHA can analyze health information as its peak rapidly and reliably, albeit with insights related to diagnosis by physicians and prognosis. In the context of telemedicine in a 5G application, this study investigates the performance of I-SAHA applications through an in-depth simulation analysis. Through its simulation, I-SAHA demonstrates that increased speed and accuracy in remote health assessment can lead to more dynamic, personalized health care. Additionally, 5G networks enable real-time transmission of vital health information to ensure prompt medical intervention with minimal intervention. The results show how telemedicine can benefit from 5G networks and how I-SAHA applications can improve healthcare. This approach has the potential to expand access to better health care by reducing existing barriers and improving public health outcomes through innovative research on telecommunications.

ADVANTAGES AND FEATURES OF USING DIGITAL TWIN TECHNOLOGY FOR STATIONARY GAS ENGINES

At the moment, there is no mass alternative to power plants based on an internal combustion engine. Such power plants are fully mastered in production, they are mobile, not bulky, and do not depend on weather conditions (wind, current, etc.), as required by wind and hydroelectric power plants. The variety and availability of fuels is also a very big advantage in favour of internal combustion engines. On the other hand, engines are a source of heat, noise and chemical pollution, and by running on fossil fuels, they contribute to negative climate change on the planet, so the issue of improving the fuel, economic and environmental efficiency of vehicle engines is relevant. With the rapid development of information technology, it is now possible to create a perfect control system for stationary engines using digital twin technology, which in the future could take over the entire task of controlling the unit with minimal human intervention, eliminating the effect of the human factor and thereby increasing the life and efficiency of the unit. The goal is to use the forecasting system to assess the actual technical condition of the engine and, based on this data, set the optimal maintenance intervals, as well as prevent accidents that entail expensive repairs or even complete replacement of the unit. Such a system would be relevant for large, expensive stationary machines involved in any technological processes where the unit's downtime entails financial losses associated with the cessation of a particular production process. Representatives of this type of power plant are gas engines - drives for booster or downhole gas compressors, gas generators operating on landfill gas or biogas.

Minimally invasive surgery, precise anesthesia and effective analgesia are crucial for walking within 1 hour after lung surgery.

The clinical effectiveness of enhanced recovery after surgery (ERAS) strategy, which emphasizes a comprehensive intervention without highlighting key points, seems to have reached a bottleneck. This study focuses on surgery, anesthesia and postoperative analgesia as the three key factors, to observe the related manifestations of ERAS in patients undergoing lung surgery with minimal intervention throughout the perioperative period. All patients who underwent lung surgery by micro-invasive video-assisted thoracoscopic surgery (VATS) at Taizhou Municipal Hospital from August 2018 to August 2019 were consecutively enrolled in the study. The clinical data of patients were collected to observe the results of ERAS. The patients were divided into intravenous analgesia group and intercostal nerve block group according to different analgesic methods, and the ERAS results of the two analgesic methods were compared. A total of 242 patients were included in the study. The time from cessation of anesthesia to extubation was 10 [interquartile range (IQR), 9, 12] minutes (min), the time from extubation to limb activity according to instructions was 18 (IQR, 14, 23) min, time to sit up was 18 (IQR, 14, 23) min, time to stand up was 40 (IQR, 35, 46) min, and time to walk was 48 (IQR, 45, 55) min. No patient had any anesthesia complications. Compared with the intravenous analgesia group, the intercostal nerve block group had shorter time to limb activity according to instructions, time to sit up and time to walk after extubation, and lower postoperative pain scores (P<0.05). For patients undergoing thoracic surgery, focusing on surgery, anesthesia and analgesia the three key factors, using micro-invasive VATS to reduce surgical trauma and shorten operation time, precise individualized anesthesia program and effective postoperative analgesia can achieve early autonomous activity of patients after surgery.

Interactive Control of a Robotic Arm using Voice Recognition

This research focuses on interactive control of a robotic arm using voice recognition, providing a user-friendly approach to controlling robotic systems through voice commands. The system is built on Arduino, integrating key components such as servo motors for precise movements, Bluetooth for wireless communication and motor drivers for higher torque applications. The HC-05 Bluetooth module supports voice recognition, enabling external devices to control the robotic arm through voice commands. This setup allows for quick responses and minimal human intervention. The proposed robotic arm supports actions such as pick and place, moving forward, backward, right, and left.

Predicting cell cycle stage from 3D single-cell nuclear-stained images.

The cell cycle governs the proliferation, differentiation, and regeneration of all eukaryotic cells. Profiling cell cycle dynamics is therefore central to basic and biomedical research spanning development, health, aging, and disease. However, current approaches to cell cycle profiling involve complex interventions that may confound experimental interpretation. To facilitate more efficient cell cycle annotation of microscopy data, we developed CellCycleNet, a machine learning (ML) workflow designed to simplify cell cycle staging with minimal experimenter intervention and cost. CellCycleNet accurately predicts cell cycle phase using only a fluorescent nuclear stain (DAPI) in fixed interphase cells. Using the Fucci2a cell cycle reporter system as ground truth, we collected two benchmarking image datasets and trained two ML models-a support vector machine (SVM) and a deep neural network-to classify nuclei as being in either the G1 or S/G2 phases of the cell cycle. Our results suggest that CellCycleNet outperforms state-of-the-art SVM models on each dataset individually. When trained on two image datasets simultaneously, CellCycleNet achieves the highest classification accuracy, with an improvement in AUROC of 0.08-0.09. The model also demonstrates excellent generalization across different microscopes, achieving an AUROC of 0.95. Overall, using features derived from 3D images, rather than 2D projections of those same images, significantly improves classification performance. We have released our image data, trained models, and software as a community resource.

Plasticity variable collagen-PEG interpenetrating networks modulate cell spreading

The extracellular matrix protein collagen I has been used extensively in the field of biomaterials due to its inherent biocompatibility and unique viscoelastic and mechanical properties. Collagen I self-assembly into fibers and networks is environmentally sensitive to gelation conditions such as temperature, resulting in gels with distinct network architectures and mechanical properties. Despite this, collagen gels are not suitable for many applications given their relatively low storage modulus. We have prepared collagen-poly(ethylene glycol) [PEG] interpenetrating network (IPN) hydrogels to reinforce the collagen network, which also induces changes to network plasticity, a recent focus of study in cell-matrix interactions. Here, we prepare collagen/PEG IPNs, varying collagen concentration and collagen gelation temperature to assess changes in microarchitecture and mechanical properties of these networks. By tuning these parameters, IPNs with a range of stiffness, plasticity and pore size are obtained. Cell studies suggest that matrix plasticity is a key determinant of cell behavior, including cell elongation, on these gels. This work presents a natural/synthetic biocompatible matrix that retains the unique structural properties of collagen networks with increased storage modulus and tunable plasticity. The described IPN materials will be of use for applications in which control of cell spreading is desirable, as only minimal changes in sample preparation lead to changes in cell spreading and circularity. Additionally, this study contributes to our understanding of the connection between collagen self-assembly conditions and matrix structural and mechanical properties and presents them as useful tools for the design of other collagen based biomaterials. Statement of significanceWe developed a collagen-poly(ethylene glycol) interpenetrating network (IPN) platform that retains native collagen architecture and biocompatibility but provides higher stiffness and tunable plasticity. With minor changes in collagen gelation temperature or concentration, IPN gels with a range of plasticity, storage modulus, and pore size can be obtained. The tunable plasticity of the gels is shown to modulate cell spreading, with a greater proportion of elongated cells on the most plastic of IPNs, supporting the assertion that matrix plasticity is a key determinant of cell spreading. The material can be of use for situations where control of cell spreading is desired with minimal intervention, and the findings herein may be used to develop similar collagen based IPN platforms.

A dental student's experiences of Minimal Intervention Dentistry

A dental student's experiences of Minimal Intervention Dentistry

A Digital Tool Supporting Pathology Practice and Identifying Leucocytes.

The aim of this work is to deliver an all-in-one package that contains both the part where the pathologist can manipulate the data as well as predefined models, altogether with the digital pathology interface with a comprehensive component that provides traceability between the identified leucocytes and the underlying possible outcomes of the potential disease. The aim is to directly provide the number of leucocytes and the mass of the cell, only from the image, with minimal intervention from the pathologist, necessary to have a PoC (Proof of Concept) or a prototype. The model was trained on a dataset of around 20,000 models, and the achieved accuracy was approximately 85%. Approximately 82% of the identified areas of interest, as determined by the models, were true positive predictions. The models correctly identified approximately 89% of the actual positive instances - areas of interest - identified by the pathologist. Approximately 6% of the total actual negative instances were incorrectly classified as positive by the models. The tool provides visual scripting, reducing the learning curve for pathology analysis techniques and offers an intuitive interface for healthcare professionals.

Two-year Outcomes of Ultra-low-dose Radiotherapy in the Treatment of Ocular Adnexal B-cell Lymphomas.

This study focuses on the efficacy and 2-year outcomes of ultra-low-dose radiotherapy (RT) in treating primary and secondary ocular adnexal lymphoma (OAL). A retrospective analysis was conducted on patients with OAL between 2017 and 2022, treated with 4 Gy of RT. The primary and secondary outcomes assessed were response rate, progression-free survival, and lymphoma-related death. Twenty-one patients with primary and secondary OAL of diverse, presentations, subtypes, and stages were included. The orbital tumors had an average size of 17 × 16 × 16 mm. Of the 14 primary OAL cases, 3 (14%) had T1N0M0 disease, 8 (38%) T2N0M0, and 3 (14%) T3N0M0 (AJCC 8th edition staging); of the 7 secondary OALs, 4 (19%) were stage IE, 2 (10%) stage IIE, and 1 (5%) stage IIIE (Ann Arbor staging). Ultra-low-dose RT yielded a 95% complete response rate and 100% progression-free survival rates, both locally and systemically at 2 years. Mild dry eyes were reported in 14% of patients as a late treatment toxicity. Ultra-low-dose RT emerges as an effective and well-tolerated treatment approach for OAL. Our findings support the use of 4 Gy, showcasing high complete response rates (95%) and durable disease control without significant local relapses over an average follow up of 27 months. Our results align with earlier investigations, validating the curative potential of ultra-low-dose RT and reinforcing the concept of achieving favorable outcomes with minimal intervention. This approach may potentially alleviate the burden of long-term ocular side effects associated with higher radiation doses, enhancing the overall quality of life for OAL patients.

AI-Driven Chatbots in CRM: Economic and Managerial Implications across Industries

In the era of digitization and technical breakthroughs, artificial intelligence (AI) has progressively found its way into the field of customer relationship management (CRM), bringing benefits as well as difficulties to businesses. AI, particularly in the context of CRM, employs machine learning (ML) and deep learning (DL) techniques to extract knowledge from data, recognize trends, make decisions, and learn from mistakes with minimal human intervention. Successful firms have effectively integrated AI into CRM for predictive analytics, computer vision, sentiment analysis, personalized recommendations, chatbots and virtual assistants, and voice and speech recognition. AI-driven chatbots, one of the AI-powered CRM systems, arose as a disruptive approach to customer service, and as such, unfolded with economic and managerial ramifications in CRM. Given the literature’s focus on other AI-driven systems, there is an obvious need for an investigation of industry applications and the implications of AI-driven chatbots in CRM. The purpose of this study is to explore and elucidate the economic and managerial implications of AI-powered chatbots within CRM systems. This investigation aims to provide a comprehensive understanding of how these technologies can enhance customer interactions, streamline business processes, and impact organizational strategies. To reach this goal, this study conducts a comparative qualitative analysis based on many interviews with experts and contributors in the field. Interviews with CRM specialists yielded insights into the use of AI-driven chatbots in CRM and their impact on the industry. The primary advantages identified in this study were the impact of AI-powered chatbots on cost, efficiency, and human performance. In addition, AI chatbots have proven useful in a variety of industries, including retail and tourism. Nonetheless, there were limitations to its usage in the healthcare system, particularly in terms of ethical problems.

How effective are non-pharmacological interventions for family caregivers? A systematic review with meta-analyses

Informal caregiving is associated with mental disorders and reduced quality of life. The aim of this systematic review was to summarize the results of methodologically high-quality intervention studies on the effectiveness of non-pharmacological interventions on patient-relevant outcomes for family caregivers in Germany. We searched three large scientific literature databases for intervention studies with a control group and a low or moderate risk of bias on the effectiveness of non-pharmacological interventions for family caregivers in Germany. Among 4,376 publications reviewed, 10 intervention studies with good methodological quality were identified. These investigated multi-component interventions or cognitive behavioral therapy for family caregivers of people with dementia (8 studies), stroke (1 study) and with general care dependency (1 study). The control groups received information material as a minimal intervention or usual standard care. Meta-analyses showed a statistically significant slight reduction in depressive symptoms 3-6 months after the start of the study as a result of cognitive behavioral therapy (standardized mean difference -0.27; 95% confidence interval -0.44 - -0.10), but this no longer reached statistical significance after 9-12 months (-0.21; -0.51 - 0.09). Multi-component interventions showed no changes in depressive symptoms either after 3-6 or after 9-12 months (-0.18; -0.40 - 0.03 and -0.14; -0.47 - 0.14, respectively). In contrast, the mental component of quality of life of family caregivers improved statistically significantly in the multi-component intervention groups compared to the control groups: slightly after 3-6 months (0.28; 0.01 - 0.56) and moderately after 9-12 months (0.45; 0.09 - 0.82). The interventions had no effect on the physical component of quality of life. The reduction of depressive symptoms by behavioral therapy interventions for family caregivers appears to be only slight and not sustainable. The mental component of quality of life of people affected may be improved in the longer term by multi-component interventions. Current scientifically examined interventions for informal caregivers do not appear to have a sufficient and sustainable effect. Greater effects could possibly be achieved through more elaborate behavioral approaches, but also structural preventive measures.

MRMQuant: Automated MRM Data Quantitation for Large-Scale Targeted Metabolomics Analysis.

Multiple reaction monitoring (MRM) is a powerful and popular technique used for metabolite quantification in targeted metabolomics. Accurate and consistent quantitation of metabolites from the MRM data is essential for subsequent analyses. Here, we developed an automated tool, MRMQuant, for targeted metabolomic quantitation using high-throughput liquid chromatography-tandem mass spectrometry MRM data to provide users with an easy-to-use tool for accurate MRM data quantitation with minimal human intervention. This tool has many user-friendly functions and features to inspect and correct the quantitation results as required. MRMQuant possesses the following features to ensure accurate quantitation: (1) dynamic signal smoothing, (2) automatic deconvolution of coeluted peaks, (3) absolute quantitation via standard curves and/or internal standards, (4) visualized inspection and correction, (5) corrections applicable to multiple samples, and (6) batch-effect correction.

(Invited) Synthetic Strategies to Lower the Barrier for Making High Quality Quantum Dot Probes

Quantum dot (QD)-based bioimaging requires QDs with exceptional optical quality and well-controlled surface properties. Although extensive research has been carried out to produce high-quality nanocrystals and to design biocompatible ligands, significant expertise is required to synthesize the best quality QDs and ligands reported. Commercially available QDs often fail to meet the required quality and suffer from irreproducibility in their performance. These restrictions have limited the wide adoption of QDs for biomedical imaging in non-expert groups. My group strives to lower the barrier to preparing high-quality QD probes in a wide range of wavelengths. In this talk, I will discuss the synthetic strategies that we have developed to systematically grow high-quality nanocrystals of various sizes and materials with minimal human intervention and simplify the synthesis of polyimidazole ligands without compromising their quality. By lowering the synthetic barrier to high-quality QDs, we envision unleashing the full potential of QDs in various fields.

Single Flap Approach Versus Conventional Flap in the Treatment of Intrabony Defects: A 6-Month Clinico Radiological Study

Abstract Introduction: The comparison of single-flap approach (SFA) and conventional flap (CF) techniques in the treatment of intrabony defects is a significant area of periodontal research. This clinical-radiological study evaluates the effectiveness of these methods over a 6-month period. SFA involves minimal surgical intervention, promoting faster healing and reduced patient discomfort. Conversely, CF is a traditional method with broader exposure to the surgical site for more extensive cleaning and repair. This study aims to provide insights into the relative advantages and clinical outcomes associated with each approach, contributing to evidence-based periodontal therapy practices. Materials and Methods: Thirty sites with chronic/aggressive periodontitis were selected for the present study. Phase I therapy was performed and evaluated after 4 weeks. Clinical parameters such as plaque and gingival index and probing pocket depth (PPD) were measured at baseline and 6 months postoperatively. The mean defect fills and percentage defect fill were calculated with the help of cone-beam computed tomography at baseline and 6 months postoperatively. The subjects were divided into two equal groups with the help of a coin toss. The Group I (test sites) underwent an SFA, whereas the Group II (control) site underwent conventional surgical procedures. Results: The PPD in both groups was reduced over a 6-month post-operative period. The mean defect size and percentage of defect fill between baseline and 6 months postoperatively in both groups were statistically significant. Conclusion: The single-flap approach and the CF are equally effective in treating periodontal intrabony defects.

The evolution of financial technology: A comprehensive bibliometric review of robo-advisors

This bibliometric study delves into the rapidly evolving domain of robo-advisors—digital platforms that offer automated, algorithm-driven financial planning services with minimal human intervention. Utilizing a robust dataset extracted from Scopus, the research employs the PRISMA flow chart methodology for meticulous screening, inclusion, and exclusion of relevant studies. Advanced bibliometric analysis tools such as Biblioshiny, VOSviewer, and CiteSpace are employed to conduct the analysis, facilitating a multifaceted examination of the literature. The findings of this study are extensive and informative, covering various aspects of the research on robo-advisors. It highlights the annual scientific production, identifying trends and growth patterns within the field. The study also spotlights the most productive authors and sources, shedding light on the leading contributors to the discourse on robo-advisors. Furthermore, it provides insights into the most globally cited documents, author cocitations, and keyword co-occurrences, revealing the core themes and discussions shaping this area of study. Significantly, the research identifies keywords with the strongest citation bursts and offers network visualizations of cited authors, showcasing the dynamic interactions within the academic community. Timezone and timeline views of cited journals and country collaborations offer a geographical and temporal perspective on the research landscape. Through these analyses, the study uncovers identified research gaps and practical implications, guiding future investigations and the practical application of robo-advisors in the financial industry.

Development of a Choreography-Based Learning Model Focused on Independent Learning

This study aims to develop a choreography-based learning model focused on independent learning to enhance students' creativity. The learning model is designed to provide students with the freedom to explore their own dance ideas and concepts with minimal intervention from instructors. The research employs a Research and Development (R&amp;D) method with a mixed-methods approach, combining both qualitative and quantitative methods. The model development process involves several stages, including needs analysis, model design, validity testing, model revision, and implementation. The research findings indicate that this learning model is effective in enhancing students' creativity. Creativity measurement is conducted using the Torrance Creativity Test, which reveals a significant increase in creativity scores among students who use this model compared to the control group. Furthermore, feedback from students and instructors suggests that this model can increase student motivation and engagement in the learning process. It is hoped that these findings will contribute positively to the development of learning methods in the field of dance and can be adapted for other disciplines that require creative approaches in their learning processes.

Reabilitação oral utilizando coroas impressas em 3D para amelogênese imperfeita: relato de caso

Amelogenesis imperfecta (AI) is a condition that severely affects tooth enamel. In most cases aesthetics and function are compromised while solutions are scarce and have high cost. The purpose of this case report is to describe a rehabilitation case of a 13-year-old male patient diagnosed with amelogenesis imperfecta. A minimally invasive oral rehabilitation was planned using digital workflow for planning, 3D printing of crowns and S-PRG fillers as an aesthetic and functional solution, with lower cost, in the face of the AI case presented. Case report: 13-year-old male with pain, sensitivity, and aesthetics complaints, the patient has been diagnosed with Amelogenesis Imperfecta Hypomaturation-Hypoplastic, Type IV (AIHHT – OMIM: 104510). Treatment was divided into three phases, preventive and sensitivity management, minimally invasive oral rehabilitation – CAD-CAM and orthodontic treatment. It is possible to conclude that digital flow and 3D printing have many benefits for dentistry such as treatment predictability, chairside and lower material waste, cost and working time. It can also be concluded that digital dentistry allows oral rehabilitation following minimal intervention principles and that it was a successful treatment with aesthetic, function and quality of life improvements.

Justicia restaurativa y delincuencia juvenil: un enfoque interdisciplinario

Crime in general is a phenomenon of wide significance and concern within the Ecuadorian territory. Proof of this is that the prison population is mostly made up of young people, which generates even more unrest. In the development of this academic work, recent research has been reviewed, as well as statistical data to evaluate the effectiveness of the scope of restorative justice in prevention and thus the reduction of recidivism rates, thus promoting the rehabilitation of young offenders. The methodology used responds to a qualitative approach, framed in legal dogmatics with a descriptive scope and a correlational design, supported by the exegetical method and bibliographic and documentary analysis. As a result, the proposal to apply restorative justice that provides viable alternatives to criminal trial is presented, aligned with the principle of minimal intervention, as one of the characteristics of Ecuadorian justice related to international standards. By specifying issues inherent to the deprivation of liberty, this work is oriented towards the de-judicialization and deinstitutionalization of sanctions, proposing a balance between the purposes, perspectives, scope and strengths through which juvenile restorative justice can face the other areas of criminal justice.